Required Qualifications:
• Must be enrolled as a student in Winter 2027 and able to complete in-person responsibilities;
• Excellent written communication skills, with the ability to critique mathematical solutions, Python code, and scholarly research reports and provide constructive feedback;
• Strong verbal communication skills and the ability to explain quantum computing concepts clearly to diverse learners and support in-class programming and tutorial exercises;
• Availability for assigned in-person office hours, staff meetings, course email monitoring, and CourseLink discussion-board support throughout Winter 2027;
• Availability during scheduled in-person lectures (Tuesday and Thursday, 3:00–4:20 PM) to support exercises, proctor CourseLink quizzes, and assist with research presentations, as assigned;
• Availability to invigilate the in-person final examination and assist with grading assignments, quizzes, research papers, presentations, and the final examination using instructor-provided criteria;
• Background in Computer Science, Mathematics, Physics, or a closely related field, with a solid foundation in algorithms, discrete mathematics, probability, and linear algebra;
• Working knowledge of complex vector spaces, inner and tensor products, eigenvalues and eigenvectors, unitary operators, and Dirac notation; ability to apply these to quantum states, gates, measurement, and entanglement;
• Knowledge of quantum circuits, computational complexity, and core algorithms, including Grover search, the quantum Fourier transform, phase estimation, and Shor’s algorithm;
• Proficiency in Python and Jupyter Notebooks, with practical Qiskit experience building, simulating, testing, and debugging quantum circuits and algorithms.
Preferred Qualifications:
• Prior successful TA or instructional experience in quantum computing, algorithms, linear algebra, machine learning, or a closely related course;
• Demonstrated quantum-computing expertise through coursework, research, curriculum development, or applied projects, particularly variational algorithms (VQE/QAOA), quantum error correction, or quantum machine learning;
• Experience with scikit-learn baselines (logistic regression/SVMs), data splits and evaluation metrics; ability to critique quantum-advantage claims, benchmarking, noise, circuit depth, and resource overhead;
• Experience mentoring research projects, reviewing LaTeX manuscripts and reproducible code, or using CourseLink and LockDown Browser; familiarity with Cirq or other quantum development tools;
• Working knowledge of PennyLane, parameterized circuits, and hybrid optimization sufficient to support introductory quantum classifiers and kernels, reproducible classical comparisons, and finite-shot and noisy simulation.
